Table tennis robots gained traction over the last years and have become a popular research challenge for control and perception algorithms. Fast and accurate ball detection is crucial for enabling a robotic arm to rally the ball back successfully. So far, most table tennis robots use conventional, frame-based cameras for the perception pipeline. However, frame-based cameras suffer from motion blur if the frame rate is not high enough for fast-moving objects. Event-based cameras, on the other hand, do not have this drawback since pixels report changes in intensity asynchronously and independently, leading to an event stream with a temporal resolution on the order of us. To the best of our knowledge, we present the first real-time perception pipeline for a table tennis robot that uses only event-based cameras. We show that compared to a frame-based pipeline, event-based perception pipelines have an update rate which is an order of magnitude higher. This is beneficial for the estimation and prediction of the ball's position, velocity, and spin, resulting in lower mean errors and uncertainties. These improvements are an advantage for the robot control, which has to be fast, given the short time a table tennis ball is flying until the robot has to hit back.

Table tennis is a fast-paced and exhilarating sport that demands agility, precision, and fast reflexes. In recent years, robotic table tennis has become a popular research challenge for robot perception algorithms. Fast and accurate ball detection is crucial for enabling a robotic arm to rally the ball back successfully. Previous approaches have employed conventional frame-based cameras with Convolutional Neural Networks (CNNs) or traditional computer vision methods. In this paper, we propose a novel solution that combines an event-based camera with Spiking Neural Networks (SNNs) for ball detection. We use multiple state-of-the-art SNN frameworks and develop a SNN architecture for each of them, complying with their corresponding constraints. Additionally, we implement the SNN solution across multiple neuromorphic edge devices, conducting comparisons of their accuracies and run-times. This furnishes robotics researchers with a benchmark illustrating the capabilities achievable with each SNN framework and a corresponding neuromorphic edge device. Next to this comparison of SNN solutions for robots, we also show that an SNN on a neuromorphic edge device is able to run in real-time in a closed loop robotic system, a table tennis robot in our use case.

Table tennis is a fast-paced and exhilarating sport that demands agility, precision, and lightning-fast reflexes. It is a sport enjoyed by millions of enthusiasts worldwide, ranging from casual players to professional athletes. In recent years, the fusion of technology and sports has led to the development of various training aids and innovations aimed at enhancing the skills of players and fostering their competitive edge. Among these technological advancements, table tennis robots have also emerged. While not yet able to compete with professional players, table tennis robots are an interesting research environment to bring perception and control algorithms towards their limits. Thus, it is not surprising, that more and more research groups use table tennis robots as a test bed for their algorithms [1][2][3][4].

Accurate calibration is crucial for using multiple cameras to triangulate the position of objects precisely. However, it is also a time-consuming process that needs to be repeated for every displacement of the cameras. The standard approach is to use a printed pattern with known geometry to estimate the intrinsic and extrinsic parameters of the cameras. The same idea can be applied to event-based cameras, though it requires extra work. By using frame reconstruction from events, a printed pattern can be detected. A blinking pattern can also be displayed on a screen. Then, the pattern can be directly detected from the events. Such calibration methods can provide accurate intrinsic calibration for both frame- and event-based cameras. However, using 2D patterns has several limitations for multi-camera extrinsic calibration, with cameras possessing highly different points of view and a wide baseline. The 2D pattern can only be detected from one direction and needs to be of significant size to compensate for its distance to the camera. This makes the extrinsic calibration time-consuming and cumbersome. To overcome these limitations, we propose eWand, a new method that uses blinking LEDs inside opaque spheres instead of a printed or displayed pattern. Our method provides a faster, easier-to-use extrinsic calibration approach that maintains high accuracy for both event- and frame-based cameras.

Event cameras are becoming increasingly popular in robotics and computer vision due to their beneficial properties, e.g., high temporal resolution, high bandwidth, almost no motion blur, and low power consumption. However, these cameras remain expensive and scarce in the market, making them inaccessible to the majority. Using event simulators minimizes the need for real event cameras to develop novel algorithms. However, due to the computational complexity of the simulation, the event streams of existing simulators cannot be generated in real-time but rather have to be pre-calculated from existing video sequences or pre-rendered and then simulated from a virtual 3D scene. Although these offline generated event streams can be used as training data for learning tasks, all response time dependent applications cannot benefit from these simulators yet, as they still require an actual event camera. This work proposes simulation methods that improve the performance of event simulation by two orders of magnitude (making them real-time capable) while remaining competitive in the quality assessment.

Spin plays a considerable role in table tennis, making a shot's trajectory harder to read and predict. However, the spin is challenging to measure because of the ball's high velocity and the magnitude of the spin values. Existing methods either require extremely high framerate cameras or are unreliable because they use the ball's logo, which may not always be visible. Because of this, many table tennis-playing robots ignore the spin, which severely limits their capabilities. This paper proposes an easily implementable and reliable spin estimation method. We developed a dotted-ball orientation estimation (DOE) method, that can then be used to estimate the spin. The dots are first localized on the image using a CNN and then identified using geometric hashing. The spin is finally regressed from the estimated orientations. Using our algorithm, the ball's orientation can be estimated with a mean error of 2.4{\deg} and the spin estimation has an relative error lower than 1%. Spins up to 175 rps are measurable with a camera of 350 fps in real time. Using our method, we generated a dataset of table tennis ball trajectories with position and spin, available on our project page.